AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Introducing roadside hazard severity indicator based on evidential reasoning approach

Safety experts have, in recent years, been attentive to roadside accident severity and occurrence. Hitherto, to prioritize road segment hazardousness, there have been little efforts to quantify a well defined indicator. In this regard, the existing indicators are usually very plain and the overall configuration of roadside is rated by experts with an exact number describing its condition. Hence, the uncertainties which come with the subjective judgments cannot be regarded as of any substance. This research contribution therefore presents a procedure to assess the road safety (roadside safety indicator) by means of the evidential reasoning (ER) approach. The betterment of ER as opposed to the available procedures for roadside safety assessment is that the proposed approach makes allowance for the uncertainties which may arise from individual judgments. Additionally, when there is a dearth of evidence concerning factors which affect roadside hazardousness severity to collate several roadside segments, this procedure will offer the benefits of utilizing the maximum/minimum utility function. With the aid of the drawn indicator, organizations and agencies responsible for ensuring road safety can reach more flexible decisions to set in-place uncertain planning and road segments priorities. This indicator can also be utilized as a variable to include roadside conditions in crash severity prediction models. A field case study has also been carried out in an attempt to follow and validate the proposed approach which is based on the run-off accident history for a sample road segments. The crash data confirm the suggested indicator.     

Assessing energy efficiencies,economy, and global warming potential (GWP) effects of major crop production systems in Iran: a case study in East Azerbaijan province

Environmental Science and Pollution Research - Efficient use of energy in farming systems is one of the most important implications for decreasing greenhouse gas (GHG) emissions and mitigating...     

Synthesis, characterization and application of ZnO-Ag as a nanophotocatalyst for organic compounds degradation, mechanism and economic study

The current work deals with ZnO-Ag nanocomposites (in the wide range of x in the Zn_1 − xO-Ag_x chemical composition) synthesized using microwave assisted solution combustion method. The structural, morphological and optical properties of the samples were characterized by XRD (X-ray diffraction), FTIR (Fourier transform infrared spectrometry), SEM (scanning electron microscopy technique), EDX (energy dispersive X-ray spectrum), ICP (inductively coupled plasma technique), TEM (transmission electron microscopy), BET (Brunauer–Emmett–Teller method), UV–Vis (ultraviolet–visible spectrophotometer) and photoluminescence spectrophotometer. The photocatalytic activity of the ZnO-Ag was investigated by photo-degradation of Acid Blue 113 (AB 113) under UV illumination in a semi-batch reactor. This experiment showed that ZnO-Ag has much more excellent photocatalytic properties than ZnO synthesized by the same method. The enhanced photocatalytic activity was due to the decrease in recombination of photogenerated electron-holes. The results showed the improvement of ZnO photocatalytic activity and there is an optimum amount of Ag (3.5 mol%) that needs to be doped with ZnO. The effect of operating parameters such as pH, catalyst dose and dye concentration were investigated. The reaction byproducts were identified by LC/MS (liquid chromatography/mass spectrometry) analysis and a pathway was proposed as well. Kinetic studies indicated that the decolorization process follows the first order kinetics. Also, the degradation percentage of AB 113 was determined using a total organic carbon (TOC) analyzer. Additionally, cost analysis of the process, the mechanism and the role of Ag were discussed.